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Item Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass(MDPI, 2015-07-28) Huang, Wenli; Sun, Guoqing; Ni, Wenjian; Zhang, Zhiyu; Dubayah, RalphAccurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha−1 or above 50% of 150 Mg·ha−1 are detectable using cross-normalized SAR data.Item Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery(MDPI, 2017-08-07) DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.Item Automated Extraction of Surface Water Extent from Sentinel-1 Data(MDPI, 2018-05-21) Huang, Wenli; DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Creed, Irena F.; Carroll, Mark L.Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.Item Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA(Springer Nature, 2015-08-16) Huang, Wenli; Swatantran, Anu; Johnson, Kristofer; Duncanson, Laura; Tang, Hao; O’Neil Dunne, Jarlath; Hurtt, George; Dubayah, RalphContinental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha−1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha−1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas. Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems.Item Implications of allometric model selection for county-level biomass mapping(Springer Nature, 2017-10-18) Duncanson, Laura; Huang, Wenli; Johnson, Kristofer; Swatantran, Anu; McRoberts, Ronald E.; Dubayah, RalphCarbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12–35, 2003), Chojnacky et al. (Forestry 87(1): 129–151, 2014) and the US Forest Service’s Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25–45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. These results confirm that allometric model selection considerably impacts biomass maps and estimates, and that allometric model errors remain poorly understood. Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies. A better understanding of the sources of allometric model errors, particularly in high biomass systems, is essential for improved forest biomass mapping.Item Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass(Multidisciplinary Digital Publishing Institute (MDPI), 2015-07-28) Huang, Wenli; Sun, Guoqing; Ni, Wenjian; Zhang, Zhiyu; Dubayah, RalphAccurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha−1 or above 50% of 150 Mg·ha−1 are detectable using cross-normalized SAR data.Item ASSESSING FOREST BIOMASS AND MONITORING CHANGES FROM DISTURBANCE AND RECOVERY WITH LIDAR AND SAR(2015) Huang, Wenli; Dubayah, Ralph; Sun, Guoqing; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation research investigated LiDAR and SAR remote sensing for assessing aboveground biomass and monitoring changes from anthropogenic forest disturbance and post-disturbance recovery. First, waveform LiDAR data were applied to map forest biomass and its changes at different key map scales for the two study sites: Howland Forest and Penobscot Experimental Forest. Results indicated that the prediction model at the scale of individual LVIS footprints is reliable when the geolocation errors are minimized. The evaluation showed that the predictions were improved markedly (20% R2 and 10% RMSE) with the increase of plot sizes from 0.25 ha to 1.0 ha. The effect of disturbance on the prediction model was strong at the footprint level but weak at the 1.0 ha plot-level. Errors reached minimum when footprint coverage approached about 50% of the area of 1.0 ha plots (16 footprints) with no improvement beyond that. Then, a sensitivity analysis was conducted for multi-source L-band SAR signatures, to change in forest biomass and related factors such as incidence angle, soil moisture, and disturbance type. The effect of incidence angle on SAR backscatter was reduced by an empirical model. A cross-image normalization was used to reduce the radiometric distortions due to changes in acquisition conditions such as soil moisture. Results demonstrated that the normalization ensured that the derived biomass of regrowth forests was cross-calibrated, and thus made the detection of biomass change possible. Further, the forest biomass was mapped for 1989, 1994 and 2009 using multi-source SAR data, and changes in biomass were derived for a 15- and a 20-year period. Results improved our understanding of issues concerning the mapping of biomass dynamic using L-ban SAR data. With the increase of plot sizes, the speckle noise and geolocations errors were reduced. Multivariable models were found to outperform the single-term models developed for biomass estimation. The main contribution of this research was an improved knowledge concerning waveform LiDAR and L-band SAR’s ability in monitoring the changes in biomass in a temperate forest. Results from this study provide calibration and validation methods as a foundation for improving the performance of current and future spaceborne systems.